Model predictive control allocation for stability improvement of four-wheel drive electric vehicles in critical driving condition

PROJECT TITLE :

Model predictive control allocation for stability improvement of four-wheel drive electric vehicles in critical driving condition

ABSTRACT:

To boost the vehicle stability of an electrical vehicle (EV) with four in-wheel motors, the authors investigate the utilization of a non-linear management allocation scheme based on model predictive management (MPC) for EVs. Such a method is helpful in yaw stabilisation of the vehicle. The proposed allocation strategy permits a modularisation of the control task, such that an upper level management system specifies a desired yaw moment to work on the EVs, whereas the control allocation is employed to see management inputs for four driving motors by commanding appropriate wheel slips. To avoid unintended facet effects, skidding or discomforting the motive force in critical driving condition, the MPC methodology, that permits us to contemplate constraints of actuating motors and slip ratio, is proposed to accommodate this challenging drawback. An analytical approach for the proposed controller is given and applied to guage the handing and stability of EVs. The experimental results show that the designed MPC allocation algorithm for motor torque has better performance in real time, and therefore the control performance can be guaranteed in the $64000-time setting.

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